For this study, an intercalation compounding method was used to prepare Chinese fir wood/Ca-montmorillonite （Ca-MMT） composite board to improve its properties such as surface mechanical properties, flame retardance ...For this study, an intercalation compounding method was used to prepare Chinese fir wood/Ca-montmorillonite （Ca-MMT） composite board to improve its properties such as surface mechanical properties, flame retardance and dimensional stability. By virtue of water-soluble phenolic resin （PF）, Chinese fir wood and Ca-MMT were mixed by pressure and vacuum impregnation. The optimum impregnation technology of Chinese fir wood/Ca-MMT composite board was obtained by using an orthogonal design and a single factor design of pressure and vacuum impregnation, using weight percent gain （WPG） as the basic index. The results are as follows： 1） On the basis of the orthogonal design and an actual experiment, the optimum preparation technology of Chinese fir wood/Ca-MMT composite board is 20% PF resin dispersion concentration （wt%）, 1.0 CEC amount of organic intercalation agent, 0.098 MPa vacuum degree, 5% concentration of Ca-MMT and 1.0 MPa pressure. 2） The WPG of the composite board samples of 450 mm length was much larger than that of the samples of 600, 750 and 900 mm length. Warm water extraction contributed little to WPG展开更多
We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as ...We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a elas- sifter, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and se- lected eight principal components to express defects. After the fusion process, we used the features to construct a data dic- tionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms, respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %.展开更多
To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focuse...To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.展开更多
We proposed a detection method for wood defects based on linear discriminant analysis (LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera, and then t...We proposed a detection method for wood defects based on linear discriminant analysis (LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera, and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%.展开更多
We studied the effect of wollastonite nanofibers on fluid flow in medium-density fiberboard （MDF）. Nanowollastonite （NW） was applied in MDF at 10 %, based on the dry weight of wood fibers. We also tested chicken f...We studied the effect of wollastonite nanofibers on fluid flow in medium-density fiberboard （MDF）. Nanowollastonite （NW） was applied in MDF at 10 %, based on the dry weight of wood fibers. We also tested chicken feathers as an additive to the matrix at 5 and 10 % by weight. The weight of feathers was reduced from the wood fibers to keep the density of the panels constant （0.66 g cm-3）. Wollastonite nanofibers acted as filler in the matrix and significantly decreased gas and liquid perme- ability. Higher thermal conductivity of the N-W-treated MDF-mats resulted in a better cure of resin, and conse- quently more integrity in the composite-matrix and lower permeability. The water-repellant property of wollastonite also contributed to the decrease in liquid permeability. Feathers reduced gas and liquid permeability due to the hydrophobic nature of keratin, as well as its formation as a physical barrier towards passing of fluids. Ten percent feather content proved too high and some checks and cracks occurred in the core of the panels after hot-pressing. Panels with 5 %-feather content resulted in both lower fluid flow and adequate physical integrity in the core sec- tion of the MDF-matrix.展开更多
基金the National Natural Science Foundation of China (Grant No.30271055)
文摘For this study, an intercalation compounding method was used to prepare Chinese fir wood/Ca-montmorillonite （Ca-MMT） composite board to improve its properties such as surface mechanical properties, flame retardance and dimensional stability. By virtue of water-soluble phenolic resin （PF）, Chinese fir wood and Ca-MMT were mixed by pressure and vacuum impregnation. The optimum impregnation technology of Chinese fir wood/Ca-MMT composite board was obtained by using an orthogonal design and a single factor design of pressure and vacuum impregnation, using weight percent gain （WPG） as the basic index. The results are as follows： 1） On the basis of the orthogonal design and an actual experiment, the optimum preparation technology of Chinese fir wood/Ca-MMT composite board is 20% PF resin dispersion concentration （wt%）, 1.0 CEC amount of organic intercalation agent, 0.098 MPa vacuum degree, 5% concentration of Ca-MMT and 1.0 MPa pressure. 2） The WPG of the composite board samples of 450 mm length was much larger than that of the samples of 600, 750 and 900 mm length. Warm water extraction contributed little to WPG
基金financially supported by the Fund of Forestry 948 Project(2011-4-04)the Fundamental Research Funds for the Central Universities(DL13CB02,DL13BB21)the Natural Science Foundation of Heilongjiang Province(C201415)
文摘We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a elas- sifter, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and se- lected eight principal components to express defects. After the fusion process, we used the features to construct a data dic- tionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms, respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %.
基金supported by the State Administration of Forestry and Grass of the 948 Project of China(Grant No.2015-4-52)the support of the Fundamental Research Funds for the Central Universities(Grant No.2572017DB05)the support of the Natural Science Foundation of Heilongjiang Province(Grant No.C2017005)
文摘To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.
基金supported by the State Forestry Administration‘‘948’’projects(2015-4-52)Fundamental Research Funds for the Central Universities(2572016BB05)+1 种基金Natural Science Foundation of Heilongjiang Province(C2015054)Heilongjiang Postdoctoral Research Fund(LBH-Q14014)
文摘We proposed a detection method for wood defects based on linear discriminant analysis (LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera, and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%.
基金supported by Shahid Rajaee Teacher Training University
文摘We studied the effect of wollastonite nanofibers on fluid flow in medium-density fiberboard （MDF）. Nanowollastonite （NW） was applied in MDF at 10 %, based on the dry weight of wood fibers. We also tested chicken feathers as an additive to the matrix at 5 and 10 % by weight. The weight of feathers was reduced from the wood fibers to keep the density of the panels constant （0.66 g cm-3）. Wollastonite nanofibers acted as filler in the matrix and significantly decreased gas and liquid perme- ability. Higher thermal conductivity of the N-W-treated MDF-mats resulted in a better cure of resin, and conse- quently more integrity in the composite-matrix and lower permeability. The water-repellant property of wollastonite also contributed to the decrease in liquid permeability. Feathers reduced gas and liquid permeability due to the hydrophobic nature of keratin, as well as its formation as a physical barrier towards passing of fluids. Ten percent feather content proved too high and some checks and cracks occurred in the core of the panels after hot-pressing. Panels with 5 %-feather content resulted in both lower fluid flow and adequate physical integrity in the core sec- tion of the MDF-matrix.
